Chapter 8make_leg = lambda start, end: Leg(start, end,
haversine(start,end))
trip = list(starmap(make_leg, pair_iter))
The legs() function creates pairs of point objects that reflect the start and end of
a leg of the voyage. Given these pairs, we can create a simple function, make_leg,
which accepts a pair of Points object, and returns a Leg object with the start point,
end point, and distance between the two points.
The benefit of the starmap(function, some_list) method is to replace a potentially
wordy (function(*args) for args in some_list) generator expression.
Cloning iterators with tee()
The tee() function gives us a way to circumvent one of the important Python rules
for working with iterables. The rule is so important, we'll repeat it here.
Iterators can be used only once.The tee() function allows us to clone an iterator. This seems to free us from
having to materialize a sequence so that we can make multiple passes over the
data. For example, a simple average for an immense dataset could be written in
the following way:
def mean(iterator):
it0, it1= tee(iterator,2)
s0= sum(1 for x in it0)
s1= sum(x for x in it1)
return s0/s1
This would compute an average without appearing to materialize the entire dataset
in memory in any form.
While interesting in principle, the tee() function's implementation suffers from
a severe limitation. In most Python implementations, the cloning is done by
materializing a sequence. While this circumvents the "one time only" rule for small
collections, it doesn't work out well for immense collections.
Also, the current implementation of the tee() function consumes the source iterator.
It might be nice to create some syntactic sugar to allow unlimited use of an iterator.
This is difficult to manage in practice. Instead, Python obliges us to optimize the
tee() function carefully.